Suppose the following situation: You are working on a product with just one production stage, for example screwing a stand to a wooden panel. For this production step, you have a machine available around the clock. You always have enough stands, wooden panels and screws as well as enough staff. Plus, your customers buy the produced table regularly. In the words of Frank Capra: It’s a Wonderful Life.
This is the hypothesis the dynamic lot-size model utilizes to determine an optimal batch size, a concept developed by Harvey M. Wagner and Thomson M. Within in the year 1958. This algorithm calculates the optimum for production under consideration of the assumptions listed above. Another prerequisite for the usage and functionality of this algorithm is the so-called Zero-Inventory-Property which states that production runs only when the stock is empty. Material for a production period is procured on demand or completely from the warehouse and processed immediately to avoid capital costs. What appears to make sense at first is, in reality, neither feasible nor efficient.
Out of touch with reality?
Neglect of restrictions (such as limited machine and resource capacity) in the dynamic lot-size model by Wagner and Within makes the application of the model as the sole approach to planning hardly possible. Bottlenecks, unforeseen orders and pseudo accurate planning are daily business for manufacturing companies, and make production managers sweat on a regular basis because customers usually do not accept delays and competitors are always on the lookout.
Creating a cost optimal master production schedule poses a major challenge. To actually keep this schedule: almost impossible. Production is rarely single stage. It often consists of a varying number of levels depending on the complexity of the product as well as a company’s organizational structure. Capacity bottlenecks and uncertainty about material availability dominate production planning, and sales are inconsistent. Long replenishment lead times and inevitable delays on the part of suppliers prevent cost optimal just-in-time procurement. So, as the delivery date moves further and further into the future, your competitiveness moves into the distance as well.
Today, planners usually create their master production schedule with the help of the concept of MRP (material requirements planning), which actually considers capacity restrictions in production. However, MRP still only relies on the step by step planning of material needs, lot size planning and the minimization of lead times, resulting in an increasingly limited solution space. The current availability of material and machine capacity remains opaque and stress situations due to manual rescheduling are to be expected with this planning approach as well.
Production managers are therefore again facing a difficult decision: to move production to an earlier point, which has consequences for the entire master production schedule (or is simply oftentimes impossible) or to push the delivery date to a later point – a no-go for customers. In both cases the planner is left with a number of problems, and short-term rescheduling contradicts every economic principle.
Half a century later
Theoretical approaches are important to understanding the problems industrial companies face and form the basis for the development of agile planning systems. But today, 58 years after the development of the dynamic lot-size model, and about 56 years after the definition of the concept of MRP, in times of digitization and individualization, the classic methods have to be modernized. This is nothing new, of course, and the technological progress is also already evident today: Calculations, which would have required two years to complete, can nowadays be solved in a matter of seconds thanks to improved hardware / computer technology. This opens up completely new possibilities in production planning as well. It is now possible to replace classic planning approaches, including their inherent shortcomings like the concept of MRP with its rigid one directional planning, with agile and flexible planning methods. These can take into account all the changing planning conditions and adapt dynamically to bring the planning process closer to reality than ever before. Speed is the key to success – but not at all costs: cost factors are incorporated into comprehensive planning methods and enable compliance of all company goals, which do not list profit as a low priority.
Because tomorrow, today will be yesterday
To plan production solely under the principle of lean management is no longer sufficient with regard to digital conversions and the industry 4.0 revolution. The objective is no longer to just eliminate storage costs by procuring material from specific production needs or to use robots for production operations. Rather, modern enterprises need to integrate and connect all areas in order to achieve maximum transparency, resulting in agile leadership competence. Specifically, this means: equipping the production for “unforeseen” events such as failure of a machine or a major short-notice order, so that the master production schedule can still be realized on time. Decision intelligence on a super-ordinate level starts exactly there and helps make companies sustainable. This way, flexibility and feasibility are no longer lost in the planning process, but remain in an always sufficiently large solution space.
Conclusion
Learning from history is also possible for the optimization of production because we can build on the theoretical approaches that Wagner and Within once developed. To be satisfied with classic action methods and only rely on step by step planning approaches, will ultimately result in a loss of competitiveness for a company. The solution is an intelligent optimization of the supply chain and production planning with the help of modern and fast algorithms. This way, you do not have to empty your warehouse to produce efficiently, but you can optimize your stock economically and produce on time and satisfy your customers.
—————————–
This article was originally written by Ludger Schuh in German and was published on the BVL blog (National Logistics Association – Germany). It was was translated by the Inventory and Supply Chain Blog Team. The original article can be read here. Some sources used in the article are in German.
